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st: Multivariate survival analysis with 3 variables analyzed as cubic splines in a competing risk setting


From   KatjaN <katja.maretty.nielsen@ki.au.dk>
To   statalist@hsphsun2.harvard.edu
Subject   st: Multivariate survival analysis with 3 variables analyzed as cubic splines in a competing risk setting
Date   Tue, 26 Mar 2013 05:47:25 -0700 (PDT)

I'm doing a survival analysis with cancer-specific mortality (DSM) calculated
as cumulative incidence and death of other diseases as competing risk. I'mm
interested in finding prognostic factors for DSM. I have 1065 patients and
361 events of interest. Based on the litterature I have chosen 6 variables
(all independent) to be included in a multivariate analysis. Among other
variables I have age, duration of symptoms, and tumorsize as continuos
variables. I would like to include these in the multivariate analysis using
cubic spline regression. There is no problem making the spline variables
using either -mkspline- or -spbase-.  I would like to make a graphical
illustration of the relation between each of the continous varialbes and
their corresponding HR (after adjusting for all the other variables). In
univariate analysis this can easily be done by using -predict- specified as
HR after the cox regression followed by at -twoway- graph, however this
seems not to work after a multivariate regression?
Furthermore I would like to compare HR for specific values of the continuous
variables, say e.g. age 15, 30, 45, 60, 75 and 90 with age 15 as the
reference. Is this possible?




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